Yeah, well, I’ve heard somewhere that correlation does not equal causation :-)
I agree that causal models are useful—if only because they make explicit certain relationships which are implicit in plain-vanilla regular models and so trip up people on a regular basis.What I’m not convinced of is that you can’t re-express that joint density on the outcomes in a conventional way even if it turns out to look a bit awkward.
Lumifer : “can we not express cause effect relationships via conditioning probabilities?”
me : “No: [example].”
Lumifer : “Ah, but this is silly because of time ordering information.”
me : “Time ordering doesn’t matter: [slight modification of example].”
Lumifer : “Yeah… causal models are useful, but it’s not clear they cannot be expressed via conditioning probabilities.”
I guess you can lead a horse to water, but you can’t make him drink. I have given you everything, all you have to do is update and move on. Or not, it’s up to you.
Yeah, well, I’ve heard somewhere that correlation does not equal causation :-)
I agree that causal models are useful—if only because they make explicit certain relationships which are implicit in plain-vanilla regular models and so trip up people on a regular basis.What I’m not convinced of is that you can’t re-express that joint density on the outcomes in a conventional way even if it turns out to look a bit awkward.
Here’s how this conversation played out.
Lumifer : “can we not express cause effect relationships via conditioning probabilities?”
me : “No: [example].”
Lumifer : “Ah, but this is silly because of time ordering information.”
me : “Time ordering doesn’t matter: [slight modification of example].”
Lumifer : “Yeah… causal models are useful, but it’s not clear they cannot be expressed via conditioning probabilities.”
I guess you can lead a horse to water, but you can’t make him drink. I have given you everything, all you have to do is update and move on. Or not, it’s up to you.
Yes, I’m a picky sort of a horse :-) Thanks for the effort, though.